Real-time systems such as industrial robots and autonomous navigation vehicles integrate a wide range of algorithms to achieve their functional behavior. In certain systems, these algorithms are deployed on dedicated single-core hardware plat- forms that exchange information over a real-time network. With the availability of current multi-core platforms, there is growing interest in an integrated architecture where these algorithms can run on a shared hardware platform. In addition, the benefits of virtualization-based cloud and fog architectures for non-real-time applications have prompted discussions about the possibility of achieving similar benefits for real-time systems. Although many useful solutions such as resource reservations and hierarchical scheduling have been proposed to facilitate hardware virtualization for real-time applications, the current state of the art is mainly concerned with applications whose timing requirements can be modelled according to the periodic or the sporadic task model. Since the computational demand of many real-time algorithms can be flexibly adjusted at runtime, e.g., by changing the periods, they can be better abstracted with the elastic task model in the context of virtualized hardware platforms. Therefore, in this paper, scheduling framework with reservations based on periodic resource supply for real-time elastic applications with single-core workloads has been proposed, and then extend this solution for applications with multi-core workloads where reservations are based on the minimum-parallelism model. Since many existing applications run on dedicated single-core platforms, simultaneously provided a systematic methodology for migrating an existing real- time software application from a single-core to a multi- core platform. In doing so, focused on recovering the architecture of the existing software and transforming it for implementation on a multi-core platform. Next, explored the advantages of a fog-based architecture over an existing robot control architecture and identify the key research challenges that must be addressed for the adoption of the fog computing architecture.
functions to hardware resources is accomplished at design time. While this approach allows software performance to be optimized on the assigned hardware resources, it limits the ability to provide continuous improvements to users.
Key words: Real time systems, Multicore platforms, Singlecore platforms, Fog computing architechture.
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